Identification using depth-based head-detection data

ABSTRACT

A candidate human head is found in depth video using a head detector. A head region of light intensity video is spatially resolved with a three-dimensional location of the candidate human head in the depth video. Facial recognition is performed on the head region of the light intensity video using a face recognizer.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 13/768,144, filed on Feb. 15, 2013, and titled “MANAGEDBIOMETRIC IDENTITY”, the entire disclosure of which is herebyincorporated herein by reference.

BACKGROUND

Some computing systems attempt to model human subjects using skeletaltracking. Skeletal tracking may serve as a basis for gesture-basedinteractions, speaker correlation, controller pairing, and otherfeatures. When skeletal tracking is unable to track a human subject,such features may not function at full fidelity.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter. Furthermore,the claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

In an embodiment, a candidate human head is found in depth video using ahead detector. A head region of a light intensity video is spatiallyresolved with a three-dimensional location of the candidate human headin the depth video. Facial recognition is performed on the head regionof the light intensity video using a face recognizer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B show an example natural user input (NUI) computingsystem imaging a human subject.

FIG. 2 shows an example pipeline for identifying and tracking a user.

FIG. 3 shows a graphical representation of an example infrared videoused to perform facial recognition by the pipeline of FIG. 2.

FIG. 4 shows a graphical representation of an example depth map used toperform head detection and skeletal modeling pipeline of FIG. 2.

FIG. 5 shows a graphical representation of an example skeletal modelproduced by the pipeline of FIG. 2.

FIG. 6 shows a graphical representation of an example candidate humanhead found in depth video.

FIG. 7 shows a graphical representation of an example head region ofinfrared video that is spatially resolved with a three-dimensionallocation of the candidate human head of FIG. 6.

FIG. 8 shows graphical representations of a plurality of example facialrecognition scans being performed on the example head region of FIG. 7.

FIG. 9 shows an example method for identifying and tracking a humansubject.

FIG. 10 shows an example computing system.

DETAILED DESCRIPTION

The present disclosure relates to a robust approach of cooperativelyusing different computer vision technologies to quickly identify andtrack human subjects. More particularly, the approach may includeperforming facial recognition on an image frame or video (e.g., a lightintensity image frame or video, such as an infrared, grayscale, or colorimage frame or video) to biometrically identify one or more humansubjects in a scene. The intensity-based facial recognition may usedepth-based, head-detection data as a seed for limiting a search area ofa light intensity image frame or video for finding human faces. Theprocess of intensity-based facial recognition may be resource intensiverelative to depth-based head detection. Thus, by performing depth-basedhead detection to limit a spatial region of a light intensity imageframe or video on which facial recognition is performed, an amount offacial recognition processing may be reduced and overall processingresource utilization may be decreased. Such a decrease in processingresource utilization may allow for facial recognition to be optionallyperformed at a higher frame rate and/or with more demanding detectionmechanisms, which may increase facial recognition accuracy. Moreover,reduced facial recognition processing may result in faster biometricidentification of a human subject, decreased energy usage, and/or theability to perform on lower power computers.

Furthermore, by relying on facial recognition seeded with depth-based,head-detection data, biometric identification of a human subject may beperformed even in scenarios where an entire body of a human subjectcannot be tracked via skeletal/body tracking. For example, in a scenariowhere biometric identification requires skeletal/body tracking, if ahuman subject is seated and deeply reclined, covered by a blanket, orotherwise occluded by a piece of furniture, then skeletal/body trackingmay be unable to lock-on to the human subject for biometricidentification. However, in such cases, the head of the human subjectmay be detectable by a depth-based head detector. Accordingly,intensity-based facial recognition and depth-based head-detection maystill be performed to provide biometric identification and tracking.

As described in more detail below, a natural user input tracking deviceincluding a depth camera, a color/grayscale/infrared camera, and/orother imaging source may be used to two- and/or three-dimensionallyimage one or more observed human subjects. Depth information and lightintensity (e.g., color/grayscale/infrared) information acquired by thetracking device may be used to efficiently and accurately model andtrack the one or more observed human subjects. In particular, the one ormore observed human subjects may be modeled as a virtual skeleton orother machine-readable body model. The virtual skeleton or othermachine-readable body model may be used as an input to control virtuallyany aspect of a computer. In this way, the computer provides a naturaluser interface that allows users to control the computer with spatialgestures.

FIGS. 1A and 1B show a nonlimiting example of a natural user input (NUI)system 100. The NUI system 100 includes a computing system 102 depictedin the illustrated example as an entertainment computing systemconfigured to play a variety of different games, other media content,and/or control or manipulate non-game applications. A display 104 may bein communication with the computing system 102. The display 104 may beconfigured to present video to human subjects, such as a game player106. A tracking device 108 may be configured to image or otherwise track(e.g., via audio) one or more human subjects, such as the game player106. For example, the tracking device 108 may include a depth camera, avisible light (e.g., RGB color) camera, an infrared camera, amicrophone, and/or other sensors configured to track observed humansubjects. In some implementations, the infrared camera may be part of adepth sensor of the tracking device 108. In one example, the depthcamera may be a structured-light depth camera. In another example, thedepth camera may be a time-of-flight depth camera.

FIGS. 1A and 1B shows a scenario in which the tracking device 108 tracksthe game player 106 so that movements of the game player 106 may beinterpreted by the computing system 102. In the illustrated example, themovements of the game player 106 are interpreted as controls that can beused to affect a video game being executed by the computing system 102.In other words, the game player 106 may use his movements to control thevideo game. The movements of the game player 106 may be interpreted asvirtually any type of game control.

The example scenario illustrated in FIG. 1A shows the game player 106playing a boxing game that is being executed by the computing system102. The display 104 visually presents a boxing opponent 110 to the gameplayer 106. Furthermore, the display 104 visually presents a playeravatar 112 that the game player 106 controls with his movements. Asshown in FIG. 1B, the game player 106 can throw a punch in a physicalspace as an instruction for the player avatar 112 to throw a punch in avirtual space of the video game. The computing system 102 and/or thetracking device 108 can be used to recognize and analyze the punch ofthe game player 106 in physical space so that the punch can beinterpreted as a game control that causes player avatar 112 to throw apunch in virtual space. For example, FIG. 1B shows the display 104visually presenting the player avatar 112 throwing a punch that strikesboxing opponent 110 responsive to the game player 106 throwing a punchin physical space.

Virtually any controllable aspect of an operating system, application,or other computing product may be controlled by movements of a humansubject. The illustrated boxing scenario is provided as an example, butis not meant to be limiting in any way. To the contrary, the illustratedscenario is intended to demonstrate a general concept, which may beapplied to a variety of different applications without departing fromthe scope of this disclosure.

The example NUI system 100 is nonlimiting. A variety of differentcomputing systems may utilize NUI information for a variety of differentpurposes without departing from the scope of this disclosure. Forexample, a NUI system may be configured to biometrically identify,recognize, analyze, and/or track one or more human subjects, such as thegame player 106 (also referred to as a human subject).

As discussed above, it may be desirable to quickly lock-on and identifyhuman subjects in a scene observed by a tracking device. Accordingly,the computing system 102 may execute a pipeline configured to performsuch operations. FIG. 2 shows an example pipeline 200. The pipeline 200may utilize a plurality of modules to perform different identifying andtracking operations. In the illustrated example, the pipeline 200includes a previously-trained, machine-learning head detector 206,previously-trained, machine-learning face recognizer 208, and apreviously-trained, machine-learning body tracker 210. It is to beunderstood that the modules of pipeline 200 alternatively may be trainedwithout machine-learning and/or otherwise configured to detect heads,faces, and/or bodies.

Each of the plurality of machine-learning modules may be previouslytrained on different ground truths to classify input data. For example,in the case of the body tracker 210, the ground truths may include aprior-trained collection of known poses. In other words, during asupervised training phase, a variety of different people are observed ina variety of different poses, and human trainers provide ground truthannotations labeling different machine-learning classifiers in theobserved data. The observed data and annotations are used to generateone or more machine-learning algorithms that map inputs (e.g.,observation data from a tracking device) to desired outputs (e.g., bodypart indices for relevant pixels).

The pipeline 200 may receive light intensity video 202 from a camera. Inone example, the light intensity video 202 may include a plurality ofimage frames of an observed scene. Each image frame may include aplurality of pixels. Each pixel may indicate an intensity of lightreflected to that pixel from a surface in the scene. Note thatreferences to light intensity video may include a single image frame ofthe light intensity video. For example, discussion of facial recognitionperformed on light intensity video may, in some cases, refer to facialrecognition performed on a single image frame of the light intensityvideo.

The light intensity video may be representative of a relative intensityof any type of visible or non-visible light. For example, the lightintensity video may be a red, green, blue (RBG) color video, a grayscalevideo, an infrared video, or another suitable visual representation oflight intensity. For the sake of simplicity, non-limiting examplesprovided herein are discussed in the context of infrared video.Regardless of the wavelength(s) of light characterized by the lightintensity video, the video may include a series of time-consecutiveframes, each frame may include a matrix of pixels, and each pixel mayrecord a light intensity value of the relevant wavelength(s) of light.

FIGS. 3-8 show example graphical representations of data at differentstages throughout the pipeline 200. The example graphicalrepresentations correspond to the game player 106 as imaged by thetracking device 108 of the NUI system 100 of FIG. 1. Underlying datacorresponding to the scene imaged by the tracking device may includeother portions (e.g., background or other users), but only the user isdepicted for ease of understanding.

FIG. 3 shows an example infrared image 300 of the game player 106 from aperspective of an infrared camera of the tracking device 108. Theinfrared image 300 may be a user-only portion of a representative imageframe of light intensity video 202 of FIG. 2, for example. The infraredimage 300 may be a false-color representation of relative infraredreflection levels of the game player 106. While FIG. 3 depicts a singleimage frame, it is to be understood that a human subject may becontinuously observed and modeled (e.g., at 30 frames per second).Accordingly, data may be collected for each such observed image frame.The collected data may be made available via one or more ApplicationProgramming Interfaces (APIs) and/or further analyzed as describedbelow.

Turning back to FIG. 2, the pipeline 200 may receive depth video 204 asinput. In one example, the depth video 204 includes a plurality of depthimage frames or depth maps of an observed scene. Each depth map mayinclude a plurality of depth pixels. Each depth pixel may indicate adepth of a surface in the scene that is imaged by that pixel. Forexample, the depth may be represented as a three-dimensional location(e.g., x/y/z coordinates or pixel address+z coordinate). Similarcoordinates may be recorded for every pixel of the depth camera. Thecoordinates for all of the pixels collectively constitute a depth map.The coordinates may be determined in any suitable manner withoutdeparting from the scope of this disclosure. For example, time offlight, structured light, or stereo imaging may be used to assess thedepth value for each of a plurality of depth pixels.

FIG. 4 shows an example depth map 400 of the game player 106 from aperspective of a depth camera of the tracking device 108. The depth map400 may be a user-only portion of a representative depth image frame ofdepth video 204 of FIG. 2, for example. The depth map 400 may be arepresentative depth map of depth video 204. The depth map 400 may be agraphical representation of depths of the various surfaces of the gameplayer 106 relative to the depth camera of the tracking device 108.While FIG. 4 depicts a single depth map, it is to be understood that ahuman subject may be continuously observed and modeled (e.g., at 30frames per second). Accordingly, data may be collected for each suchdepth map. The collected data may be made available via one or moreApplication Programming Interfaces (APIs) and/or further analyzed asdescribed below.

The light intensity video 202 and the depth video 204 may be at leastpartially spatially registered with each other. In the above describedexample implementation, the light intensity video 202 and the depthvideo 204 may be received from the tracking device 108. In particular,the tracking device 108 may include an infrared camera and a depthcamera that have the same resolutions, although this is not required.Whether the cameras have the same or different resolutions, the pixelsof the infrared camera may be registered to the pixels of the depthcamera. In other implementations, the depth video and the infrared videomay be received from other suitable sources having differentresolutions/perspectives, and a suitable spatial-registration operationmay be performed to identify a common frame of reference between thedepth video and the infrared video. In either scenario, both infraredand depth information may be determined for each portion of an observedscene by considering the registered pixels from the infrared camera andthe depth camera. The infrared video and the depth video may be receivedfrom any suitable sources in any suitable manner.

Continuing with FIG. 2, the previously-trained, machine-learning headdetector 206 may be configured to find candidate human heads in thedepth video 204. In one example implementation, the head detector 206may be configured to classify depth pixels of the depth video 204 with aprobability that a particular depth pixel corresponds to a human head.This type of head-only determination may be performed withoutconsidering whether a depth pixel corresponds to a body part other thanthe head. In other words, the head detector 206 determines whether ornot a depth pixel corresponds to a human head, but is not concerned withwhat different body part the pixel images if the pixel does not image ahuman head. Since, the head detector 206 classifies the depth pixelsaccording to two classes (e.g., “a human head” and “not a human head”),classification may be faster and/or more accurate relative to othermachine-learning modules that attempt to classify all body parts. Thefast and accurate processing allows the head detector to be run on everydepth pixel in an image frame of the depth video 204 in order to findcandidate human heads in a timely manner.

Once the depth pixels of the depth video 204 have been classified by thehead detector 206, the head detector may be configured to identify anycandidate human head(s) 212 in the depth video 204. In one exampleimplementation, a candidate human head may include a contiguous regionof depth pixels each having a probability of being a human head that isgreater than a threshold. The threshold may be set to any suitablevalue. In one example, the threshold may be set based on the particularground truth used to train the head detector 206. In a scenario wherethe depth video 204 includes a plurality of candidate human heads, thehead detector may find and designate each candidate human headseparately. A candidate human head may be determined by the headdetector 206 in any suitable manner.

Each candidate human head 212 may be associated with a three-dimensionallocation 214 in the depth video 204. In one example, thethree-dimensional location 214 may include x/y/z coordinates. In anotherexample, the three-dimensional location 214 may include a plurality ofx/y/z coordinates and/or x/y/z offsets that define a boundary in whichthe candidate human head is contained. The three-dimensional location214 of each candidate human head may be output from the head detector206 to the face recognizer 208 and/or the body tracker 210 to seedfurther identifying and tracking processing by these modules.

FIG. 6 shows an example graphical representation of a candidate humanhead 600 that may be found in the depth video 204 by the head detector206. The candidate human head 600 may include a plurality of depthpixels that are classified as having a probability of being a human headthat is above a threshold. The candidate human head 600 may beassociated with a three-dimensional location 602. In the illustratedexample, the three-dimensional location is defined as an x/y/zcoordinate and a Δx, Δy, Δz offset that collectively define a boundaryin which the candidate human head is contained.

Turning back to FIG. 2, the previously-trained, machine-learning facerecognizer 208 may use the three-dimensional location 214 of eachcandidate human head to spatially resolve a head region 216 of the lightintensity video 202. The head region 216 may define a limited portion ofthe light intensity video 202 that is less than all of the infraredvideo. In one example, the head region 216 corresponds to a bounding boxin a two-dimensional image frame of the light intensity video 202. Forexample, the bounding box may include all infrared pixels correspondingto depth pixels in the depth video 204 that correspond to the candidatehuman head. The face recognizer 208 may be configured to perform facialrecognition on the head region of the candidate human head in order toidentify a human face. The face recognizer 208 may be configured toperform facial recognition on all of the head regions of the lightintensity video 202 without performing facial recognition on otherportions of the infrared video outside of the head regions.

In one example, the face recognizer 208 may be configured to performfacial recognition using machine-learning classifiers trained to resolveone or more facial parameters (e.g., eye-to-eye spacing, eye-to-nosespacing, head size,). The face recognizer 208 may be configured tocompare identified facial parameters to a database of facial parametersfor known users, and thus attribute a recognized face to a particularindividual. For example, a set of facial features may be compared to aplurality of different sets of facial features of different known humansubjects (e.g., associated with user identities or profiles) in order toidentify the human face 218. Any suitable facial recognition algorithmmay be implemented to recognize a human face in the head region.

The face recognizer 208 may be configured to repeatedly scan the headregion using different size bounding rectangles. In one example, thefacial recognition scan may begin with a minimum size boundingrectangle, and the size of the bounding rectangle may be increased eachsubsequent scan until a face is detected. In another example, the facialrecognition scan may begin with a maximum size bounding rectangle, andthe size of the bounding rectangle may be decreased until a face isdetected. The maximum and/or minimum size bounding rectangles may beselected so as to accommodate the range of human anatomy, withoutunnecessarily scanning areas of the infrared image that are unlikely toinclude a human face. If the scans of the head region do not positivelyidentify a human face, the face recognizer 208 may scan other regions ofthe infrared video 202 and/or face identification may be bypassedaltogether.

In some implementations, depth information corresponding to thecandidate human head may be provided from the head detector 206 to theface recognizer 208 to further reduce an amount of facial recognitionperformed by the face recognizer. In particular, as described above, theface recognizer 208 may perform a plurality of scans of the head regionsusing different size bounding rectangles. Optionally, the facerecognizer 208 may be configured to estimate the minimum size boundingrectangle and/or the maximum size bounding rectangle based on the depthinformation. For example, an average human face size may be scaled as afunction of a distance of the candidate human head relative to the depthcamera. Further, a delta may be applied to the scaled, average humanface size to accommodate for small faces (e.g., children) and big facesin order to generate a small face estimate and a large face estimate. Inother words, the small face estimate and the large face estimate eachmay be scaled as a function of distance derived from the depth video204.

FIG. 7 shows an example graphical representation of a two-dimensionalhead region 700 of an infrared image frame 702 of the infrared video202. The head region 700 may be spatially resolved with thethree-dimensional location 602 of the candidate human head 600 in thedepth video 204 by the face recognizer 208. The face recognizer 208performs facial recognition on the pixels located inside the head region700 without performing facial recognition on the other pixels of theinfrared image frame 702 located outside of the head region. Although asingle head region is shown in this example, a plurality of head regionsmay be spatial resolved in the infrared image frame 702, and the facerecognizer 208 may perform facial recognition on each head region inorder to identify all faces in the infrared image frame 702.

FIG. 8 shows the example graphical representation of the two-dimensionalhead region 700 of the infrared image frame 702 being scanned usingdifferent sized bounding rectangles. The head region 700 may beinitially scanned using the bounding rectangle 800 that is sizedaccording to a small face estimate. The size of the bounding rectanglemay be incrementally increased with each subsequent scan of the headregion. For example, the head region 700 may be subsequently scannedusing a bounding rectangle 802 that is sized according to an averageface estimate. If a human face is not identified in the boundingrectangle 804, then a subsequent scan of the head region may use abounding rectangle 806 that is sized according to the large faceestimate.

By limiting the spatial area of image frames of the infrared video onwhich facial recognition is performed by the face recognizer 208, anamount of facial recognition processing may be reduced. Such a reductionmay allow for additional processing resources to be made available forother purposes. For example, a frame rate of facial recognition may beincreased.

Moreover, by using depth information as a seed to determine an area ofan infrared image frame on which to perform facial recognition, facialtopography may be used as a check to ensure that candidates are actuallylive human beings. As such, photographs, portraits, and other planarsurfaces that have images of human faces may be eliminated fromconsideration by the head detector. Since such images of faces are noteven passed to the face recognizer, the images of faces are not detectedas false positives and processing resources are not wasted.

Continuing with FIG. 2, the previously-trained, machine-learning bodytracker 210 may be configured to perform skeletal modeling on a bodyregion 220 of the depth video 204 to produce a skeletal or othermachine-readable body model. The body region 220 may be spatiallycontiguous with the three-dimensional location 214 of the candidatehuman head. The body region 220 may define a limited portion of thedepth video 204. In one example, the body tracker 210 may be configuredto analyze the depth pixels of the body region 220 of the depth video204 in order to determine what part of the human subject's body eachsuch pixel is likely to image. A variety of different body-partassignment techniques can be used to assess which part of a humansubject's body a particular pixel is likely to image. Each pixel of thebody region 220 may be assigned a body part index as classified by thebody tracker 210. For example, the body part index may include adiscrete identifier, confidence value, and/or body part probabilitydistribution indicating the body part, or parts, to which that pixel islikely to image. Body part indices may be determined, assigned, andsaved in any suitable manner without departing from the scope of thisdisclosure. The collection of body parts may comprise a skeletal model222.

FIG. 5 shows a graphical representation of a virtual skeleton 500 (alsoreferred to as a skeletal model) that serves as a machine-readablerepresentation of the game player 106. The virtual skeleton 500 includestwenty virtual joints—{head, shoulder center, spine, hip center, rightshoulder, right elbow, right wrist, right hand, left shoulder, leftelbow, left wrist, left hand, right hip, right knee, right ankle, rightfoot, left hip, left knee, left ankle, and left foot}. This twenty jointvirtual skeleton is provided as a nonlimiting example. Virtual skeletonsin accordance with the present disclosure may have virtually any numberof joints.

The various skeletal joints may correspond to actual joints of a humansubject, centroids of the human subject's body parts, terminal ends of ahuman subject's extremities, and/or points without a direct anatomicallink to the human subject. Each joint has at least three degrees offreedom (e.g., world space x, y, z). As such, each joint of the virtualskeleton is defined with a three-dimensional position. The virtualskeleton 500 may optionally include a plurality of virtual bones. Thevarious skeletal bones may extend from one skeletal joint to another andmay correspond to actual bones, limbs, or portions of bones and/or limbsof a human subject. The skeletal model 500 may track motion of the gameplayer 106 throughout the depth video 204.

In some implementations, the body tracker 210 may be configured toreceive the three-dimensional location 214 of the candidate human head212 from the head detector 206. Further, the body tracker 210 may beconfigured to constrain a head joint of the skeletal model 222 to thethree-dimensional location 214 of the candidate human head 212 asidentified by the head detector 206. By constraining the head joint tothe three-dimensional location 214, accuracy may be increased and orprocessing may be decreased.

In some implementations, the body tracker 210 may perform skeletalmodeling and the face recognizer 208 may perform facial recognition inparallel in order to provide faster biometric identification andtracking lock-on. In particular, such parallel processing may beenabled, because the face recognizer 208 is seeded with head detectiondata that provides a head region on which to perform facial recognition.In other words, the face recognizer 208 does not have to wait for thebody tracker 210 to provide a head region in order to begin facialrecognition.

Furthermore, in some cases, the skeletal model 222 may be produced bythe body tracker 210 before the human face 218 is positively identifiedby the face recognizer 208. Once a face is identified, the facerecognizer 208 may be configured to associate the skeletal model 222with the human face 218. For example, the positively identified humanface may be associated with a user identity or player index that allowsmotion or gestures recognized from the skeletal model to be attributedto the user identity.

FIG. 9 shows an example method 900 for identifying and tracking a humansubject. For example, the method 900 may be performed by the computingsystem 102 shown in FIG. 1 or the computing system 1000 shown in FIG.10. At 902, the method 900 may include receiving a depth video. Forexample, the depth video may be provided from a depth camera of atracking device, such as tracking device 108 shown in FIG. 1 or inputsubsystem 1008 shown in FIG. 10.

At 904, the method 900 may include receiving a light intensity video.For example, the light intensity video may be an infrared video providedfrom an infrared camera of a tracking device, such as tracking device108 shown in FIG. 1 or input subsystem 1008 shown in FIG. 10. The lightintensity video may be at least partially spatially-registered to thedepth video.

At 906, the method 900 may include finding a candidate human head in thedepth video using a previously-trained, machine-learning head detector,such as head detector 206 shown in FIG. 2.

At 908, the method 900 may include spatially resolving a head region ofthe light intensity video with a three-dimensional location of thecandidate human head in the depth video. In one example, the candidatehuman head may include a contiguous region of depth pixels each having aprobability that is greater than a threshold of being a human head asclassified by the head detector. Further, in one example, the headregion may define a limited portion of the light intensity video. Forexample, the head region may define only the portion of thetwo-dimensional image frame that is spatially resolved to thethree-dimensional location of the candidate human head in the depthvideo.

At 910, the method 900 may include performing facial recognition on thehead region of the light intensity video using a previously-trainedmachine-learning face recognizer, such as the face recognizer 208 shownin FIG. 2.

At 912, the method 900 may include performing skeletal modeling on abody region of the depth video using a previously-trained,machine-learning body tracker to produce a skeletal model, such as thebody tracker 210 shown in FIG. 2. The body region may be spatiallycontiguous with the three-dimensional location of the candidate humanhead. The body region may define a limited portion of the depth video.

In some implementations, the skeletal modeling (912) and the facialrecognition (910) may be performed in parallel. In some implementations,the skeletal modeling may seed the facial recognition, and in someimplementations the facial recognition may seed the skeletal modeling.

At 914, the method 900 may include determining whether a positiveidentification of a human face in the head region is produced by theface recognizer. If a positive identification of a human face in thehead region is produced by the face recognizer, then the method 900moves to 916. Otherwise, other identification strategies may beemployed, or identification may be bypassed.

At 916, the method 900 may include responsive to the face recognizerproducing a positive identification of a human face in the head regionof the light intensity video, associating the skeletal model with thehuman face.

By performing depth-based head detection to limit a spatial region oflight intensity video on which facial recognition is performed, anamount of facial recognition processing may be reduced and overallprocessing resource utilization may be decreased. Because a head trackerseeds the facial recognition, as opposed to a full body tracker,processing is further reduced. In this way, biometric identification andtracking lock-on may be achieved in a faster and more robust manner.

In some embodiments, the methods and processes described herein may betied to a computing system of one or more computing devices. Inparticular, such methods and processes may be implemented as acomputer-application program or service, an application-programminginterface (API), a library, and/or other computer-program product.

FIG. 10 schematically shows a non-limiting embodiment of a computingsystem 1000 that can enact one or more of the methods and processesdescribed above. Computing system 1000 is shown in simplified form.Computing system 1000 may take the form of one or more personalcomputers, server computers, tablet computers, home-entertainmentcomputers, network computing devices, gaming devices, mobile computingdevices, mobile communication devices (e.g., smart phone), and/or othercomputing devices.

Computing system 1000 includes a logic machine 1002 and a storagemachine 1004. Computing system 1000 may optionally include a displaysubsystem 1006, input subsystem 1008, communication subsystem 1010,and/or other components not shown in FIG. 10.

Logic machine 1002 includes one or more physical devices configured toexecute instructions. For example, the logic machine may be configuredto execute instructions that are part of one or more applications,services, programs, routines, libraries, objects, components, datastructures, or other logical constructs. Such instructions may beimplemented to perform a task, implement a data type, transform thestate of one or more components, achieve a technical effect, orotherwise arrive at a desired result.

The logic machine 1002 may include one or more processors configured toexecute software instructions. Additionally or alternatively, the logicmachine may include one or more hardware or firmware logic machinesconfigured to execute hardware or firmware instructions. Processors ofthe logic machine may be single-core or multi-core, and the instructionsexecuted thereon may be configured for sequential, parallel, and/ordistributed processing. Individual components of the logic machineoptionally may be distributed among two or more separate devices, whichmay be remotely located and/or configured for coordinated processing.Aspects of the logic machine may be virtualized and executed by remotelyaccessible, networked computing devices configured in a cloud-computingconfiguration.

Storage machine 1004 includes one or more physical devices configured tohold instructions executable by the logic machine to implement themethods and processes described herein. When such methods and processesare implemented, the state of storage machine 1004 may betransformed—e.g., to hold different data.

Storage machine 1004 may include removable and/or built-in devices.Storage machine 1004 may include optical memory (e.g., CD, DVD, HD-DVD,Blu-Ray Disc, etc.), semiconductor memory (e.g., RAM, EPROM, EEPROM,etc.), and/or magnetic memory (e.g., hard-disk drive, floppy-disk drive,tape drive, MRAM, etc.), among others. Storage machine 1004 may includevolatile, nonvolatile, dynamic, static, read/write, read-only,random-access, sequential-access, location-addressable,file-addressable, and/or content-addressable devices.

It will be appreciated that storage machine 1004 includes one or morephysical devices. However, aspects of the instructions described hereinalternatively may be propagated by a communication medium (e.g., anelectromagnetic signal, an optical signal, etc.) that is not held by aphysical device for a finite duration.

Aspects of logic machine 1002 and storage machine 1004 may be integratedtogether into one or more hardware-logic components. Such hardware-logiccomponents may include field-programmable gate arrays (FPGAs), program-and application-specific integrated circuits (PASIC/ASICs), program- andapplication-specific standard products (PSSP/ASSPs), system-on-a-chip(SOC), and complex programmable logic devices (CPLDs), for example.

The terms “module,” “program,” and “engine” may be used to describe anaspect of computing system 1000 implemented to perform a particularfunction. In some cases, a module, program, or engine may beinstantiated via logic machine 1002 executing instructions held bystorage machine 1004. It will be understood that different modules,programs, and/or engines may be instantiated from the same application,service, code block, object, library, routine, API, function, etc.Likewise, the same module, program, and/or engine may be instantiated bydifferent applications, services, code blocks, objects, routines, APIs,functions, etc. The terms “module,” “program,” and “engine” mayencompass individual or groups of executable files, data files,libraries, drivers, scripts, database records, etc.

When included, display subsystem 1006 may be used to present a visualrepresentation of data held by storage machine 1004. This visualrepresentation may take the form of a graphical user interface (GUI). Asthe herein described methods and processes change the data held by thestorage machine, and thus transform the state of the storage machine,the state of display subsystem 1006 may likewise be transformed tovisually represent changes in the underlying data. Display subsystem1006 may include one or more display devices utilizing virtually anytype of technology. Such display devices may be combined with logicmachine 1002 and/or storage machine 1004 in a shared enclosure, or suchdisplay devices may be peripheral display devices.

When included, input subsystem 1008 may comprise or interface with oneor more user-input devices such as a keyboard, mouse, touch screen, orgame controller. In some embodiments, the input subsystem may compriseor interface with selected natural user input (NUI) componentry. Suchcomponentry may be integrated or peripheral, and the transduction and/orprocessing of input actions may be handled on- or off-board. Example NUIcomponentry may include a microphone for speech and/or voicerecognition; an infrared, color, stereoscopic, and/or depth camera formachine vision and/or gesture recognition; a head tracker, eye tracker,accelerometer, and/or gyroscope for motion detection and/or intentrecognition; as well as electric-field sensing componentry for assessingbrain activity.

When included, communication subsystem 1010 may be configured tocommunicatively couple computing system 1000 with one or more othercomputing devices. Communication subsystem 1010 may include wired and/orwireless communication devices compatible with one or more differentcommunication protocols. As non-limiting examples, the communicationsubsystem may be configured for communication via a wireless telephonenetwork, or a wired or wireless local- or wide-area network. In someembodiments, the communication subsystem may allow computing system 1000to send and/or receive messages to and/or from other devices via anetwork such as the Internet.

An example provides, on a computing system, a method comprisingreceiving a depth video, receiving a light intensity video at leastpartially spatially-registered to the depth video, finding a candidatehuman head in the depth video using a head detector, spatially resolvinga head region of the light intensity video with a three-dimensionallocation of the candidate human head in the depth video, the head regiondefining a limited portion of the light intensity video, and performingfacial recognition on the head region of the light intensity video usinga face recognizer. Optionally, the method further comprises performingskeletal modeling on a body region of the depth video using a bodytracker to produce a skeletal model. The body region may be spatiallycontiguous with the three-dimensional location of the candidate humanhead, and the body region may define a limited portion of the depthvideo. Optionally, the skeletal modeling and the facial recognition maybe performed in parallel. Optionally, the method further comprisesresponsive to the face recognizer producing a positive identification ofa human face in the head region of the light intensity video,associating the skeletal model with the human face. Optionally, the bodytracker may be configured to constrain a head joint of the skeletalmodel to the three-dimensional location of the candidate human head asidentified by the head detector. Optionally, the head detector may beconfigured to classify depth pixels of the depth video by producing foreach depth pixel a probability that the depth pixel corresponds to ahuman head without producing a probability that the depth pixelcorresponds to another body part. The candidate human head may include acontiguous region of depth pixels each having a probability this isgreater than a threshold. Optionally, the face recognizer may beconfigured to repeatedly scan the head region inside a boundingrectangle. A size of the bounding rectangle may change each scan.Optionally, the bounding rectangle may be scaled as a function of adepth of the candidate human head. Any or all of the above-describedexamples may be combined in any suitable manner in variousimplementations.

Another example, provides a computing system comprising a logic machine,and a storage machine holding instruction executable by the logicmachine to receive a depth video, receive an infrared video at leastpartially spatially-registered to the depth video, find a candidatehuman head in the depth video using a previously-trained,machine-learning head detector, spatially resolve a head region of theinfrared video with a three-dimensional location of the candidate humanhead in the depth video, the head region defining a limited portion ofthe infrared video, perform facial recognition on the head region of theinfrared video using a previously-trained, machine-learning facerecognizer, and perform skeletal modeling on a body region of the depthvideo using a previously-trained, machine-learning body tracker toproduce a skeletal model, the body region being spatially contiguouswith the three-dimensional location of the candidate human head, and thebody region defining a limited portion of the depth video. Optionally,the skeletal modeling and the facial recognition are performed inparallel. Optionally, the storage machine may further hold instructionsexecutable by the logic machine to responsive to the previously-trained,machine-learning face recognizer producing a positive identification ofa human face in the head region of the infrared video, associate theskeletal model with the human face. Optionally, the previously-trained,machine-learning body tracker may be configured to constrain a headjoint of the skeletal model to the three-dimensional location of thecandidate human head as identified by the previously-trained,machine-learning head detector. Optionally, the previously-trained,machine-learning head detector may be configured to classify depthpixels of the depth video by producing for each depth pixel aprobability that the depth pixel corresponds to a human head withoutproducing a probability that the depth pixel corresponds to another bodypart. The candidate human may head include a contiguous region of depthpixels each having a probability this is greater than a threshold.Optionally the previously-trained, machine-learning face recognizer maybe configured to repeatedly scan the head region inside a boundingrectangle. A size of the bounding rectangle may change each scan.Optionally, the bounding rectangle may be scaled as a function of adepth of the candidate human head. Any or all of the above-describedexamples may be combined in any suitable manner in variousimplementations.

Another example provides a computing system comprising a logic machine,and a storage machine holding instruction executable by the logicmachine to receive a depth video, receive an infrared video at leastpartially spatially-registered to the depth video, find a candidatehuman head in the depth video using a previously-trained,machine-learning head detector, spatially resolve a head region of theinfrared video with a three-dimensional location of the candidate humanhead in the depth video, the head region defining a limited portion ofthe infrared video, perform facial recognition on the head region of theinfrared video using a previously-trained, machine-learning facerecognizer, perform skeletal modeling on a body region of the depthvideo using a previously-trained, machine-learning body tracker toproduce a skeletal model, the previously-trained, machine-learning bodytracker being configured to constrain a head joint of the skeletal modelto the three-dimensional location of the candidate human head asidentified by the previously-trained, machine-learning head detector.The body region may be spatially contiguous with the three-dimensionallocation of the candidate human head, and the body region defining alimited portion of the depth video. Optionally, the storage machine mayfurther hold instructions executable by the logic machine to responsiveto the previously-trained, machine-learning face recognizer producing apositive identification of a human face in the head region of theinfrared video, associate the skeletal model with the human face.Optionally, the previously-trained, machine-learning head detector maybe configured to classify depth pixels of the depth video by producingfor each depth pixel a probability that the depth pixel corresponds to ahuman head without producing a probability that the depth pixelcorresponds to another body part. The candidate human head may include acontiguous region of depth pixels each having a probability this isgreater than a threshold. Optionally, the previously-trained,machine-learning face recognizer may be configured to repeatedly scanthe head region inside a bounding rectangle. A size of the boundingrectangle may change each scan. The bounding rectangle may be scaled asa function of a depth of the candidate human head. Optionally, theskeletal modeling and the facial recognition may be performed inparallel. Any or all of the above-described examples may be combined inany suitable manner in various implementations.

It will be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificembodiments or examples are not to be considered in a limiting sense,because numerous variations are possible. The specific routines ormethods described herein may represent one or more of any number ofprocessing strategies. As such, various acts illustrated and/ordescribed may be performed in the sequence illustrated and/or described,in other sequences, in parallel, or omitted. Likewise, the order of theabove-described processes may be changed.

The subject matter of the present disclosure includes all novel andnonobvious combinations and subcombinations of the various processes,systems and configurations, and other features, functions, acts, and/orproperties disclosed herein, as well as any and all equivalents thereof.

The invention claimed is:
 1. On a computing system, a method comprising:receiving a depth video; receiving a light intensity video at leastpartially spatially-registered to the depth video; finding a candidatehuman head in the depth video using a head detector; spatially resolvinga head region of the light intensity video with a three-dimensionallocation of the candidate human head in the depth video, the head regiondefining a limited portion of the light intensity video; and performingfacial recognition on only the head region of the light intensity videousing a face recognizer.
 2. The method of claim 1, further comprising:performing skeletal modeling on a body region of the depth video using abody tracker to produce a skeletal model, the body region beingspatially contiguous with the three-dimensional location of thecandidate human head, and the body region defining a limited portion ofthe depth video.
 3. The method of claim 2, wherein the skeletal modelingand the facial recognition are performed in parallel.
 4. The method ofclaim 2, further comprising: responsive to the face recognizer producinga positive identification of a human face in the head region of thelight intensity video, associating the skeletal model with the humanface.
 5. The method of claim 2, wherein the body tracker is configuredto constrain a head joint of the skeletal model to the three-dimensionallocation of the candidate human head as identified by the head detector.6. The method of claim 1, wherein the head detector is configured toclassify depth pixels of the depth video by producing for each depthpixel a probability that the depth pixel corresponds to a human headwithout producing a probability that the depth pixel corresponds toanother body part, and wherein the candidate human head includes acontiguous region of depth pixels each having a probability this isgreater than a threshold.
 7. The method of claim 1, wherein the facerecognizer is configured to repeatedly scan the head region inside abounding rectangle, a size of the bounding rectangle changing each scan.8. The method of claim 7, wherein the bounding rectangle is scaled as afunction of a depth of the candidate human head.
 9. A computing system,comprising: a logic machine; and a storage machine holding instructionexecutable by the logic machine to: receive a depth video; receive aninfrared video at least partially spatially-registered to the depthvideo; find a candidate human head in the depth video using apreviously-trained, machine-learning head detector; spatially resolve ahead region of the infrared video with a three-dimensional location ofthe candidate human head in the depth video, the head region defining alimited portion of the infrared video; perform facial recognition ononly the head region of the infrared video using a previously-trained,machine-learning face recognizer; and perform skeletal modeling on abody region of the depth video using a previously-trained,machine-learning body tracker to produce a skeletal model, the bodyregion being spatially contiguous with the three-dimensional location ofthe candidate human head, and the body region defining a limited portionof the depth video.
 10. The computing system of claim 9, wherein theskeletal modeling and the facial recognition are performed in parallel.11. The computing system of claim 9, wherein the storage machine furtherholds instructions executable by the logic machine to: responsive to thepreviously-trained, machine-learning face recognizer producing apositive identification of a human face in the head region of theinfrared video, associate the skeletal model with the human face. 12.The computing system of claim 9, wherein the previously-trained,machine-learning body tracker is configured to constrain a head joint ofthe skeletal model to the three-dimensional location of the candidatehuman head as identified by the previously-trained, machine-learninghead detector.
 13. The computing system of claim 9, wherein thepreviously-trained, machine-learning head detector is configured toclassify depth pixels of the depth video by producing for each depthpixel a probability that the depth pixel corresponds to a human headwithout producing a probability that the depth pixel corresponds toanother body part, and wherein the candidate human head includes acontiguous region of depth pixels each having a probability this isgreater than a threshold.
 14. The computing system of claim 9, whereinthe previously-trained, machine-learning face recognizer is configuredto repeatedly scan the head region inside a bounding rectangle, a sizeof the bounding rectangle changing each scan.
 15. The computing systemof claim 14, wherein the bounding rectangle is scaled as a function of adepth of the candidate human head.
 16. A computing system, comprising: alogic machine; and a storage machine holding instruction executable bythe logic machine to: receive a depth video; receive an infrared videoat least partially spatially-registered to the depth video; find acandidate human head in the depth video using a previously-trained,machine-learning head detector; spatially resolve a head region of theinfrared video with a three-dimensional location of the candidate humanhead in the depth video, the head region defining a limited portion ofthe infrared video; perform facial recognition on only the head regionof the infrared video using a previously-trained, machine-learning facerecognizer; perform skeletal modeling on a body region of the depthvideo using a previously-trained, machine-learning body tracker toproduce a skeletal model, the previously-trained, machine-learning bodytracker being configured to constrain a head joint of the skeletal modelto the three-dimensional location of the candidate human head asidentified by the previously-trained, machine-learning head detector,the body region being spatially contiguous with the three-dimensionallocation of the candidate human head, and the body region defining alimited portion of the depth video.
 17. The computing system of claim16, wherein the storage machine further holds instructions executable bythe logic machine to: responsive to the previously-trained,machine-learning face recognizer producing a positive identification ofa human face in the head region of the infrared video, associate theskeletal model with the human face.
 18. The computing system of claim16, wherein the previously-trained, machine-learning head detector isconfigured to classify depth pixels of the depth video by producing foreach depth pixel a probability that the depth pixel corresponds to ahuman head without producing a probability that the depth pixelcorresponds to another body part, and wherein the candidate human headincludes a contiguous region of depth pixels each having a probabilitythis is greater than a threshold.
 19. The computing system of claim 16,wherein the previously-trained, machine-learning face recognizer isconfigured to repeatedly scan the head region inside a boundingrectangle, a size of the bounding rectangle changing each scan, andwherein the bounding rectangle is scaled as a function of a depth of thecandidate human head.
 20. The computing system of claim 16, whereinfinding the candidate human head in the depth video is performed priorto performing facial recognition on the infrared video, and wherein theskeletal modeling and the facial recognition are performed in parallel.